Research on Event Accumulator Settings for Event-Based SLAM

This is the source code for paper "Research on Event Accumulator Settings for Event-Based SLAM". For more details please refer to https://arxiv.org/abs/2112.00427

1. Prerequisites

See dv_ros and VINS-Fusion

2. Build

cd ~/catkin_ws/src
git clone https://github.com/robin-shaun/event-slam-accumulator-settings.git
cd ../
catkin_make # or catkin build
source ~/catkin_ws/devel/setup.bash

3. Demo

We evaluate the proposed method quantitatively on the Event Camera Dataset. This demo takes the dynamic_6dof sequence as example.

First, start dv_ros. Notice that the event accumulator depends on the timestamp, so when you restart the dataset or davis driver, you should restart dv_ros.

roslaunch dv_ros davis240.launch

And then, start VINS-Fusion

roslaunch vins vins_rviz.launch

rosrun vins vins_node ~/catkin_ws/src/VINS-Fusion/config/davis/rpg_240_mono_imu_config.yaml

Finally, play the rosbag

cd ~/catkin_ws/src/event-slam-accumulator-settings/dataset
rosbag play dynamic_6dof.bag

Notice that the default frequency of VINS-Fusion is the same as the event frame frequency, 30 Hz. If your CPU is not strong enough, maybe you should decrease it to 15 Hz in this file by uncommenting the code. However, this will decrease the performance as well.

        // if(inputImageCnt % 2 == 0)
        // {
            mBuf.lock();
            featureBuf.push(make_pair(t, featureFrame));
            mBuf.unlock();
        // }

4. Run with your devices

We have tested the code with DAVIS240 and DAVIS346. If you want to run with your devices, you should use rpg_dvs_ros. The most important thing to do is calibrating the event camera and imu. We advise to use Kalibr with traditional image from APS and IMU, because the intrinsics and extrinsics are almost the same for APS and DVS.

If you want to compare the event-based VINS Fusion with traditional VINS Fusion with DAVIS346, you should use this code. Because the frame from APS of DAVIS346 sometimes changes the size, we do some modification for VINS-Fusion.

5. Run with ORBSLAM3 for Stereo Visual SLAM

Event frame based stereo visual SLAM is not introduced in the paper. We use ORBSLAM3 to process the event frames from dv_ros. The dataset is stereo DAVIS dataset.The result shows that the proposed method performs better than ESVO by computing absolute trajectory error (RMS, unit: cm), using Python package for the evaluation of odometry and SLAM.

Event window size: 15000, Event contribution: 0.33

monitor

bin

box

desk

First compile ORBSLAM3 with ROS according to this. And then you can use this script to run ORBSLAM3, which subscribes event frames and publish estimated poses (We modify ORBSLAM3 a little to publish estimated poses).

6. Evalute results

We modify rpg_trajectory_evaluation to print mean position error and mean yaw error in the terminal. You can evalute results showed in the paper by

python analyze_trajectory_single.py ../results/boxes_6dof

7. Acknowledgements

Thanks for dv_ros and all the open source projects we use.